# dh = DataHandler()
dh.merge_csvs_on_first_time_overlap(master,
slave,
out_path=None,
merge_column=None,
master_columns=['bx', 'by', 'bz'],
slave_columns=['tx', 'ty', 'tz'],
rearrange_columns_to=None,
save=False,
left_index=True,
right_index=True)
dh.add_columns_based_on_csv(label,
columns_name=["label"],
join_type="inner")
if idx == 0:
merged_df = dh.get_dataframe_iterator()
continue
merged_old_shape = merged_df.shape
# vertically stack the dataframes aka add the rows from dataframe2 as rows to the dataframe1
merged_df = dh_stacker.vertical_stack_dataframes(
merged_df, dh.get_dataframe_iterator(), set_as_current_df=False)
print("shape merged df: ", merged_df.shape, "should be ",
dh.get_dataframe_iterator().shape, " more than old ",
merged_old_shape)
print("Final merge form: ", merged_df.shape)
'3': [['2018-04-24', '14:08:01', '15:08:00']]
})
###################################### remove rows that does not have label ###########################
df1 = dh1.get_dataframe_iterator()
df2 = dh2.get_dataframe_iterator()
print(df1.shape, df2.shape)
df1.dropna(subset=['label'], inplace=True)
df2.dropna(subset=['label'], inplace=True)
print(df1.shape, df2.shape)
############################## THEN COMBINE INTO ONE BIG TRAINING SET AKA VERTICAL STACKING #############
dataframe = dh1.vertical_stack_dataframes(df1, df2, set_as_current_df=False)
# dataframe = dh1.vertical_stack_dataframes(dataframe, df3, set_as_current_df=False)
print("DATAFRAME\n", dataframe.head(5), dataframe.shape)
############################## THEN WE MUST EXTRACT FEATURES N LABELS ######################################
pipeObj = Pipeline()
back_feat_train, thigh_feat_train, label_train = pipeObj.get_features_and_labels_as_np_array(
dataframe)
############################## THEN WE MUST TRAIN THE CLASSIFIER ######################################
RFC = models.get("RFC", {})
##############
# MODEL ARGUMENTS